{"product_id":"python-machine-learning-isbn-9781119545637","title":"Python Machine Learning","description":"\u003cp\u003e\u003cb\u003ePython makes machine learning easy for beginners and experienced developers\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWith computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart—it requires a good foundation in statistics, as well as programming knowledge. \u003ci\u003ePython Machine Learning\u003c\/i\u003e will help coders of all levels master one of the most in-demand programming skillsets in use today.\u003c\/p\u003e \u003cp\u003eReaders will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand.\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePython data science—manipulating data and data visualization\u003c\/li\u003e \u003cli\u003eData cleansing\u003c\/li\u003e \u003cli\u003eUnderstanding Machine learning algorithms\u003c\/li\u003e \u003cli\u003eSupervised learning algorithms\u003c\/li\u003e \u003cli\u003eUnsupervised learning algorithms\u003c\/li\u003e \u003cli\u003eDeploying machine learning models\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003ePython Machine Learning\u003c\/i\u003e is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level.\u003c\/p\u003e \u003cp\u003eIntroduction xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Introduction to Machine Learning 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Machine Learning? 2\u003c\/p\u003e \u003cp\u003eWhat Problems Will Machine Learning Be Solving in This Book? 3\u003c\/p\u003e \u003cp\u003eClassification 4\u003c\/p\u003e \u003cp\u003eRegression 4\u003c\/p\u003e \u003cp\u003eClustering 5\u003c\/p\u003e \u003cp\u003eTypes of Machine Learning Algorithms 5\u003c\/p\u003e \u003cp\u003eSupervised Learning 5\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 7\u003c\/p\u003e \u003cp\u003eGetting the Tools 8\u003c\/p\u003e \u003cp\u003eObtaining Anaconda 8\u003c\/p\u003e \u003cp\u003eInstalling Anaconda 9\u003c\/p\u003e \u003cp\u003eRunning Jupyter Notebook for Mac 9\u003c\/p\u003e \u003cp\u003eRunning Jupyter Notebook for Windows 10\u003c\/p\u003e \u003cp\u003eCreating a New Notebook 11\u003c\/p\u003e \u003cp\u003eNaming the Notebook 12\u003c\/p\u003e \u003cp\u003eAdding and Removing Cells 13\u003c\/p\u003e \u003cp\u003eRunning a Cell 14\u003c\/p\u003e \u003cp\u003eRestarting the Kernel 16\u003c\/p\u003e \u003cp\u003eExporting Your Notebook 16\u003c\/p\u003e \u003cp\u003eGetting Help 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Extending Python Using NumPy 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is NumPy? 19\u003c\/p\u003e \u003cp\u003eCreating NumPy Arrays 20\u003c\/p\u003e \u003cp\u003eArray Indexing 22\u003c\/p\u003e \u003cp\u003eBoolean Indexing 22\u003c\/p\u003e \u003cp\u003eSlicing Arrays 23\u003c\/p\u003e \u003cp\u003eNumPy Slice Is a Reference 25\u003c\/p\u003e \u003cp\u003eReshaping Arrays 26\u003c\/p\u003e \u003cp\u003eArray Math 27\u003c\/p\u003e \u003cp\u003eDot Product 29\u003c\/p\u003e \u003cp\u003eMatrix 30\u003c\/p\u003e \u003cp\u003eCumulative Sum 31\u003c\/p\u003e \u003cp\u003eNumPy Sorting 32\u003c\/p\u003e \u003cp\u003eArray Assignment 34\u003c\/p\u003e \u003cp\u003eCopying by Reference 34\u003c\/p\u003e \u003cp\u003eCopying by View (Shallow Copy) 36\u003c\/p\u003e \u003cp\u003eCopying by Value (Deep Copy) 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Manipulating Tabular Data Using Pandas 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Pandas? 39\u003c\/p\u003e \u003cp\u003ePandas Series 40\u003c\/p\u003e \u003cp\u003eCreating a Series Using a Specified Index 41\u003c\/p\u003e \u003cp\u003eAccessing Elements in a Series 41\u003c\/p\u003e \u003cp\u003eSpecifying a Datetime Range as the Index of a Series 42\u003c\/p\u003e \u003cp\u003eDate Ranges 43\u003c\/p\u003e \u003cp\u003ePandas DataFrame 45\u003c\/p\u003e \u003cp\u003eCreating a DataFrame 45\u003c\/p\u003e \u003cp\u003eSpecifying the Index in a DataFrame 46\u003c\/p\u003e \u003cp\u003eGenerating Descriptive Statistics on the DataFrame 47\u003c\/p\u003e \u003cp\u003eExtracting from DataFrames 49\u003c\/p\u003e \u003cp\u003eSelecting the First and Last Five Rows 49\u003c\/p\u003e \u003cp\u003eSelecting a Specific Column in a DataFrame 50\u003c\/p\u003e \u003cp\u003eSlicing Based on Row Number 50\u003c\/p\u003e \u003cp\u003eSlicing Based on Row and Column Numbers 51\u003c\/p\u003e \u003cp\u003eSlicing Based on Labels 52\u003c\/p\u003e \u003cp\u003eSelecting a Single Cell in a DataFrame 54\u003c\/p\u003e \u003cp\u003eSelecting Based on Cell Value 54\u003c\/p\u003e \u003cp\u003eTransforming DataFrames 54\u003c\/p\u003e \u003cp\u003eChecking to See If a Result Is a DataFrame or Series 55\u003c\/p\u003e \u003cp\u003eSorting Data in a DataFrame 55\u003c\/p\u003e \u003cp\u003eSorting by Index 55\u003c\/p\u003e \u003cp\u003eSorting by Value 56\u003c\/p\u003e \u003cp\u003eApplying Functions to a DataFrame 57\u003c\/p\u003e \u003cp\u003eAdding and Removing Rows and Columns in a DataFrame 60\u003c\/p\u003e \u003cp\u003eAdding a Column 61\u003c\/p\u003e \u003cp\u003eRemoving Rows 61\u003c\/p\u003e \u003cp\u003eRemoving Columns 62\u003c\/p\u003e \u003cp\u003eGenerating a Crosstab 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Data Visualization Using matplotlib 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is matplotlib? 67\u003c\/p\u003e \u003cp\u003ePlotting Line Charts 68\u003c\/p\u003e \u003cp\u003eAdding Title and Labels 69\u003c\/p\u003e \u003cp\u003eStyling 69\u003c\/p\u003e \u003cp\u003ePlotting Multiple Lines in the Same Chart 71\u003c\/p\u003e \u003cp\u003eAdding a Legend 72\u003c\/p\u003e \u003cp\u003ePlotting Bar Charts 73\u003c\/p\u003e \u003cp\u003eAdding Another Bar to the Chart 74\u003c\/p\u003e \u003cp\u003eChanging the Tick Marks 75\u003c\/p\u003e \u003cp\u003ePlotting Pie Charts 77\u003c\/p\u003e \u003cp\u003eExploding the Slices 78\u003c\/p\u003e \u003cp\u003eDisplaying Custom Colors 79\u003c\/p\u003e \u003cp\u003eRotating the Pie Chart 80\u003c\/p\u003e \u003cp\u003eDisplaying a Legend 81\u003c\/p\u003e \u003cp\u003eSaving the Chart 82\u003c\/p\u003e \u003cp\u003ePlotting Scatter Plots 83\u003c\/p\u003e \u003cp\u003eCombining Plots 83\u003c\/p\u003e \u003cp\u003eSubplots 84\u003c\/p\u003e \u003cp\u003ePlotting Using Seaborn 85\u003c\/p\u003e \u003cp\u003eDisplaying Categorical Plots 86\u003c\/p\u003e \u003cp\u003eDisplaying Lmplots 88\u003c\/p\u003e \u003cp\u003eDisplaying Swarmplots 90\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Getting Started with Scikit-learn for Machine Learning 93\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction to Scikit-learn 93\u003c\/p\u003e \u003cp\u003eGetting Datasets 94\u003c\/p\u003e \u003cp\u003eUsing the Scikit-learn Dataset 94\u003c\/p\u003e \u003cp\u003eUsing the Kaggle Dataset 97\u003c\/p\u003e \u003cp\u003eUsing the UCI (University of California, Irvine) Machine Learning Repository 97\u003c\/p\u003e \u003cp\u003eGenerating Your Own Dataset 98\u003c\/p\u003e \u003cp\u003eLinearly Distributed Dataset 98\u003c\/p\u003e \u003cp\u003eClustered Dataset 98\u003c\/p\u003e \u003cp\u003eClustered Dataset Distributed in Circular Fashion 100\u003c\/p\u003e \u003cp\u003eGetting Started with Scikit-learn 100\u003c\/p\u003e \u003cp\u003eUsing the LinearRegression Class for Fitting the Model 101\u003c\/p\u003e \u003cp\u003eMaking Predictions 102\u003c\/p\u003e \u003cp\u003ePlotting the Linear Regression Line 102\u003c\/p\u003e \u003cp\u003eGetting the Gradient and Intercept of the Linear Regression Line 103\u003c\/p\u003e \u003cp\u003eExamining the Performance of the Model by Calculating the Residual Sum of Squares 104\u003c\/p\u003e \u003cp\u003eEvaluating the Model Using a Test Dataset 105\u003c\/p\u003e \u003cp\u003ePersisting the Model 106\u003c\/p\u003e \u003cp\u003eData Cleansing 107\u003c\/p\u003e \u003cp\u003eCleaning Rows with NaNs 108\u003c\/p\u003e \u003cp\u003eReplacing NaN with the Mean of the Column 109\u003c\/p\u003e \u003cp\u003eRemoving Rows 109\u003c\/p\u003e \u003cp\u003eRemoving Duplicate Rows 110\u003c\/p\u003e \u003cp\u003eNormalizing Columns 112\u003c\/p\u003e \u003cp\u003eRemoving Outliers 113\u003c\/p\u003e \u003cp\u003eTukey Fences 113\u003c\/p\u003e \u003cp\u003eZ-Score 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Supervised Learning—Linear Regression 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTypes of Linear Regression 119\u003c\/p\u003e \u003cp\u003eLinear Regression 120\u003c\/p\u003e \u003cp\u003eUsing the Boston Dataset 120\u003c\/p\u003e \u003cp\u003eData Cleansing 125\u003c\/p\u003e \u003cp\u003eFeature Selection 126\u003c\/p\u003e \u003cp\u003eMultiple Regression 128\u003c\/p\u003e \u003cp\u003eTraining the Model 131\u003c\/p\u003e \u003cp\u003eGetting the Intercept and Coefficients 133\u003c\/p\u003e \u003cp\u003ePlotting the 3D Hyperplane 133\u003c\/p\u003e \u003cp\u003ePolynomial Regression 135\u003c\/p\u003e \u003cp\u003eFormula for Polynomial Regression 138\u003c\/p\u003e \u003cp\u003ePolynomial Regression in Scikit-learn 138\u003c\/p\u003e \u003cp\u003eUnderstanding Bias and Variance 141\u003c\/p\u003e \u003cp\u003eUsing Polynomial Multiple Regression on the Boston Dataset 144\u003c\/p\u003e \u003cp\u003ePlotting the 3D Hyperplane 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Supervised Learning—Classification Using Logistic Regression 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Logistic Regression? 151\u003c\/p\u003e \u003cp\u003eUnderstanding Odds 153\u003c\/p\u003e \u003cp\u003eLogit Function 153\u003c\/p\u003e \u003cp\u003eSigmoid Curve 154\u003c\/p\u003e \u003cp\u003eUsing the Breast Cancer Wisconsin (Diagnostic) Data Set 156\u003c\/p\u003e \u003cp\u003eExamining the Relationship Between Features 156\u003c\/p\u003e \u003cp\u003ePlotting the Features in 2D 157\u003c\/p\u003e \u003cp\u003ePlotting in 3D 158\u003c\/p\u003e \u003cp\u003eTraining Using One Feature 161\u003c\/p\u003e \u003cp\u003eFinding the Intercept and Coefficient 162\u003c\/p\u003e \u003cp\u003ePlotting the Sigmoid Curve 162\u003c\/p\u003e \u003cp\u003eMaking Predictions 163\u003c\/p\u003e \u003cp\u003eTraining the Model Using All Features 164\u003c\/p\u003e \u003cp\u003eTesting the Model 166\u003c\/p\u003e \u003cp\u003eGetting the Confusion Matrix 166\u003c\/p\u003e \u003cp\u003eComputing Accuracy, Recall, Precision, and Other Metrics 168\u003c\/p\u003e \u003cp\u003eReceiver Operating Characteristic (ROC) Curve 171\u003c\/p\u003e \u003cp\u003ePlotting the ROC and Finding the Area Under the Curve (AUC) 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Supervised Learning—Classification Using Support Vector Machines 177\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is a Support Vector Machine? 177\u003c\/p\u003e \u003cp\u003eMaximum Separability 178\u003c\/p\u003e \u003cp\u003eSupport Vectors 179\u003c\/p\u003e \u003cp\u003eFormula for the Hyperplane 180\u003c\/p\u003e \u003cp\u003eUsing Scikit-learn for SVM 181\u003c\/p\u003e \u003cp\u003ePlotting the Hyperplane and the Margins 184\u003c\/p\u003e \u003cp\u003eMaking Predictions 185\u003c\/p\u003e \u003cp\u003eKernel Trick 186\u003c\/p\u003e \u003cp\u003eAdding a Third Dimension 187\u003c\/p\u003e \u003cp\u003ePlotting the 3D Hyperplane 189\u003c\/p\u003e \u003cp\u003eTypes of Kernels 191\u003c\/p\u003e \u003cp\u003eC 194\u003c\/p\u003e \u003cp\u003eRadial Basis Function (RBF) Kernel 196\u003c\/p\u003e \u003cp\u003eGamma 197\u003c\/p\u003e \u003cp\u003ePolynomial Kernel 199\u003c\/p\u003e \u003cp\u003eUsing SVM for Real-Life Problems 200\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Supervised Learning—Classification Using K-Nearest Neighbors (KNN) 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is K-Nearest Neighbors? 205\u003c\/p\u003e \u003cp\u003eImplementing KNN in Python 206\u003c\/p\u003e \u003cp\u003ePlotting the Points 206\u003c\/p\u003e \u003cp\u003eCalculating the Distance Between the Points 207\u003c\/p\u003e \u003cp\u003eImplementing KNN 208\u003c\/p\u003e \u003cp\u003eMaking Predictions 209\u003c\/p\u003e \u003cp\u003eVisualizing Different Values of K 209\u003c\/p\u003e \u003cp\u003eUsing Scikit-Learn’s KNeighborsClassifier Class for KNN 211\u003c\/p\u003e \u003cp\u003eExploring Different Values of K 213\u003c\/p\u003e \u003cp\u003eCross-Validation 216\u003c\/p\u003e \u003cp\u003eParameter-Tuning K 217\u003c\/p\u003e \u003cp\u003eFinding the Optimal K 218\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Unsupervised Learning—Clustering Using K-Means 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Unsupervised Learning? 221\u003c\/p\u003e \u003cp\u003eUnsupervised Learning Using K-Means 222\u003c\/p\u003e \u003cp\u003eHow Clustering in K-Means Works 222\u003c\/p\u003e \u003cp\u003eImplementing K-Means in Python 225\u003c\/p\u003e \u003cp\u003eUsing K-Means in Scikit-learn 230\u003c\/p\u003e \u003cp\u003eEvaluating Cluster Size Using the Silhouette Coefficient 232\u003c\/p\u003e \u003cp\u003eCalculating the Silhouette Coefficient 233\u003c\/p\u003e \u003cp\u003eFinding the Optimal K 234\u003c\/p\u003e \u003cp\u003eUsing K-Means to Solve Real-Life Problems 236\u003c\/p\u003e \u003cp\u003eImporting the Data 237\u003c\/p\u003e \u003cp\u003eCleaning the Data 237\u003c\/p\u003e \u003cp\u003ePlotting the Scatter Plot 238\u003c\/p\u003e \u003cp\u003eClustering Using K-Means 239\u003c\/p\u003e \u003cp\u003eFinding the Optimal Size Classes 240\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Using Azure Machine Learning Studio 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Microsoft Azure Machine Learning Studio? 243\u003c\/p\u003e \u003cp\u003eAn Example Using the Titanic Experiment 244\u003c\/p\u003e \u003cp\u003eUsing Microsoft Azure Machine Learning Studio 246\u003c\/p\u003e \u003cp\u003eUploading Your Dataset 247\u003c\/p\u003e \u003cp\u003eCreating an Experiment 248\u003c\/p\u003e \u003cp\u003eFiltering the Data and Making Fields Categorical 252\u003c\/p\u003e \u003cp\u003eRemoving the Missing Data 254\u003c\/p\u003e \u003cp\u003eSplitting the Data for Training and Testing 254\u003c\/p\u003e \u003cp\u003eTraining a Model 256\u003c\/p\u003e \u003cp\u003eComparing Against Other Algorithms 258\u003c\/p\u003e \u003cp\u003eEvaluating Machine Learning Algorithms 260\u003c\/p\u003e \u003cp\u003ePublishing the Learning Model as a Web Service 261\u003c\/p\u003e \u003cp\u003ePublishing the Experiment 261\u003c\/p\u003e \u003cp\u003eTesting the Web Service 263\u003c\/p\u003e \u003cp\u003eProgrammatically Accessing the Web Service 263\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Deploying Machine Learning Models 269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDeploying ML 269\u003c\/p\u003e \u003cp\u003eCase Study 270\u003c\/p\u003e \u003cp\u003eLoading the Data 271\u003c\/p\u003e \u003cp\u003eCleaning the Data 271\u003c\/p\u003e \u003cp\u003eExamining the Correlation Between the Features 273\u003c\/p\u003e \u003cp\u003ePlotting the Correlation Between Features 274\u003c\/p\u003e \u003cp\u003eEvaluating the Algorithms 277\u003c\/p\u003e \u003cp\u003eLogistic Regression 277\u003c\/p\u003e \u003cp\u003eK-Nearest Neighbors 277\u003c\/p\u003e \u003cp\u003eSupport Vector Machines 278\u003c\/p\u003e \u003cp\u003eSelecting the Best Performing Algorithm 279\u003c\/p\u003e \u003cp\u003eTraining and Saving the Model 279\u003c\/p\u003e \u003cp\u003eDeploying the Model 280\u003c\/p\u003e \u003cp\u003eTesting the Model 282\u003c\/p\u003e \u003cp\u003eCreating the Client Application to Use the Model 283\u003c\/p\u003e \u003cp\u003eIndex 285\u003c\/p\u003e \u003cb\u003eWei-Meng Lee\u003c\/b\u003e is a technologist and founder of Developer Learning Solutions (http:\/\/www.learn2develop.net), a technology company specializing in hands-on training on the latest mobile technologies. Wei-Meng has many years of training experiences and his training courses place special emphasis on the learning-by-doing approach. His hands-on approach to learning programming makes understanding the subject much easier than reading books, tutorials, and documentations. His name regularly appears in online and print publications such as DevX.com, MobiForge.com, and \u003ci\u003eCoDe Magazine\u003c\/i\u003e.","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989892546789,"sku":"NP9781119545637","price":42.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119545637.jpg?v=1761785818","url":"https:\/\/k12savings.com\/products\/python-machine-learning-isbn-9781119545637","provider":"K12savings","version":"1.0","type":"link"}